Knowledge catalogue
arXiv:2507.06529v2 Announce Type: replace Abstract: Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate ha
arXiv:2610.07911v1 Announce Type: cross Abstract: Despite recent advances in video generation, motion customization remains challenging due to content leakage, where appearance attributes from the ref
arXiv:2610.07981v1 Announce Type: new Abstract: Higher-order models (e.g., hypergraph neural networks) often outperform lower-order baselines on hypergraph learning benchmarks, and their advantages ar
arXiv:2610.07816v1 Announce Type: new Abstract: Small language models (SLMs) are attractive as local agent controllers because they reduce remote inference, latency, and deployment footprint, yet stru
arXiv:2610.07497v1 Announce Type: new Abstract: Muon combines current and past gradients into matrix momentum. For M=USigma V^op, the idealized polar update Q=UV^op gives every singular direction the
arXiv:2603.04364v2 Announce Type: replace-cross Abstract: Multimodal web agents that process both screenshots and accessibility trees are increasingly deployed to interact with web interfaces, yet the
arXiv:2610.07928v1 Announce Type: cross Abstract: Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities. However, existing
arXiv:2610.08726v1 Announce Type: cross Abstract: Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a
arXiv:2610.07675v1 Announce Type: new Abstract: AI agents are entering production in increasingly consequential environments without a shared semantic standard for what their evaluations actually mean
arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while pr
arXiv:2610.08432v1 Announce Type: new Abstract: Improving embodied agents often focuses on optimizing the underlying model through training, while the surrounding agent harness that controls planning,
arXiv:2610.05370v2 Announce Type: replace Abstract: Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside
arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders
arXiv:2610.07359v1 Announce Type: new Abstract: Runtime gates for agent tool calls are stacked on the assumption that their errors multiply. We test it on 1,119 labelled agent actions from three corpo
arXiv:2610.06890v1 Announce Type: new Abstract: We present an industry experience report on three years of operating an event-driven cloud infrastructure for continuous machine learning training in au
arXiv:2610.06970v1 Announce Type: new Abstract: Egocentric bimanual hand pose estimation is important for virtual interaction, wearable control, and rehabilitation, but visual observations are often d
arXiv:2610.08660v1 Announce Type: new Abstract: Background: Biomedical AI can generate plausible explanations without reliably verifying whether each statement is supported by patient-specific evidenc
arXiv:2610.08367v1 Announce Type: new Abstract: Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity al
arXiv:2610.07692v1 Announce Type: new Abstract: Human video offers a scalable source of experience for dexterous robot learning, but obtaining motion and tactile supervision while preserving bare hand
arXiv:2610.08363v1 Announce Type: new Abstract: Maritime systems operate in highly dynamic environments where unexpected equipment failures can compromise safety, reliability, and operational efficien
arXiv:2610.07786v1 Announce Type: new Abstract: Pathwise gradients are preferred for continuous random variables because they are unbiased, low variance, and work with a single sample. For discrete va
arXiv:2610.07898v1 Announce Type: new Abstract: Repository-level software engineering (SWE) is a challenging long-horizon setting: agents must reason over extended interactions, use tools, and adapt t
arXiv:2610.07399v1 Announce Type: new Abstract: Federated deep clustering seeks to learn clustering-friendly representations from decentralized unlabeled data while preserving client privacy. However,
arXiv:2508.10020v2 Announce Type: replace-cross Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especially
arXiv:2610.08574v1 Announce Type: cross Abstract: Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment
arXiv:2603.21276v2 Announce Type: replace-cross Abstract: The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE
arXiv:2610.08637v1 Announce Type: new Abstract: Quadruped robots are increasingly expected to carry objects while moving through human environments. But what happens when a person interacts directly w
arXiv:2610.07079v1 Announce Type: new Abstract: Accurate bioactivity prediction is a central challenge in early-stage drug discovery, as individual assays often contain too few measurements to train r
arXiv:2610.08639v1 Announce Type: new Abstract: As generated images become increasingly realistic, reliable forgery detection is essential for maintaining trust in visual information. However, existin
arXiv:2610.08388v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on know
arXiv:2610.07550v1 Announce Type: cross Abstract: Random access (RA) is one of the most foundational medium access control (MAC) layer scheduling schemes for handling unpredictable data traffic from m
arXiv:2511.20510v3 Announce Type: replace Abstract: Molecule generation from extremely limited training data is a key challenge in drug discovery. Existing fragment-based methods are more suitable tha
arXiv:2610.07783v1 Announce Type: new Abstract: Bird's-eye-view (BEV) representations have become a widely used interface between perception and planning in autonomous driving, but they encode what is
arXiv:2610.08381v1 Announce Type: new Abstract: Human video offers a scalable source of robot demonstrations, yet most human-to-humanoid retargeting methods assume a legged robot with human-like kinem
arXiv:2610.08538v1 Announce Type: cross Abstract: Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting introd
arXiv:2610.06910v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in web front-end execution, with browser-based game generation
arXiv:2604.00055v2 Announce Type: replace-cross Abstract: Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabiliti
arXiv:2603.16273v3 Announce Type: replace Abstract: For field robotic missions such as inspection, search-and-rescue, and exploration, light detection and ranging (LiDAR)-inertial odometry (LIO) can s
arXiv:2610.06882v1 Announce Type: new Abstract: We study assignment quality in 3D heterogeneous multi-agent reach-avoid games and identify a recurring failure mode of cardinality-only matching in geom
arXiv:2610.08401v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information a
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, ex
arXiv:2610.06978v1 Announce Type: new Abstract: Gaussian Belief Propagation (GBP) is a distributed inference algorithm that passes messages in graphical models, making it attractive for scalable spati
arXiv:2610.08350v1 Announce Type: cross Abstract: Visual world models enable goal-directed control through decision-time action search, but their deployment efficiency is often limited by conservative
arXiv:2610.08320v1 Announce Type: new Abstract: Cluttered indoor environments, where large and heavy objects are scattered across diverse surfaces, require humanoid robots to sequentially navigate, gr
arXiv:2610.07243v1 Announce Type: new Abstract: Breast cancer is the leading cause of cancer-related mortality among women in Sub-Saharan Africa, where delayed diagnosis results from limited radiology
arXiv:2610.07366v1 Announce Type: new Abstract: Robust person re-identification often combines complementary cues such as face, gait, and body shape. While adaptive fusion typically targets query qual
arXiv:2610.08120v1 Announce Type: new Abstract: Humanoid robots operating in unstructured environments must combine robust whole-body control with the ability to perceive and physically interact with
arXiv:2610.07791v1 Announce Type: new Abstract: Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tend
arXiv:2610.06729v2 Announce Type: replace Abstract: Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative s
arXiv:2506.17874v3 Announce Type: replace-cross Abstract: In many real-world applications, ensuring the robustness and stability of deep neural networks (DNNs) is crucial, particularly for image class
arXiv:2610.07704v1 Announce Type: cross Abstract: Fully decentralized multi-agent reinforcement learning (MARL), also referred to as independent learning, requires each agent to learn and act using on
arXiv:2512.03683v2 Announce Type: replace Abstract: 3D stylization is central to game development, virtual reality, and digital arts, where the demand for diverse assets calls for scalable methods tha
arXiv:2610.07906v1 Announce Type: new Abstract: We study sequential content sufficiency by investigating whether a representation retains the ordered target information available in its input. An info
arXiv:2610.07663v1 Announce Type: cross Abstract: User behavior simulation is the computational modeling of user interactions within information systems through the use of simulated agents in place of
arXiv:2610.08413v1 Announce Type: cross Abstract: Large language models routinely answer questions that cannot be answered from the information given, and in dialogue they answer before enough has bee
arXiv:2610.07587v1 Announce Type: new Abstract: LLM orchestration investigates how an orchestrator coordinates a group of autonomous agents to achieve common goals or maximize collective welfare. The
arXiv:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models ma
arXiv:2610.07015v1 Announce Type: new Abstract: Privileged 3D supervision uses additional geometric information during training to guide RGB-based visuomotor policy learning, without requiring geometr
arXiv:2610.07562v1 Announce Type: cross Abstract: Learning an ensemble of GFlowNets to sample from a discrete target distribution has become a common approach for achieving better state space explorat
arXiv:2602.10013v2 Announce Type: replace Abstract: Successfully manipulating many everyday objects, such as potato chips, requires precise force regulation. Failure to modulate force can lead to task